A source sentence \(\mathbf {S}\)  is represented by vectors of its tokens \(\mathbf {x}_1, \mathbf {x}_2,....,\mathbf {x}_n\) , where \(\mathbf {x}_i \in \mathbb {R}^{(d_w+d_f)}\)  is the vector representation of the \(i\) th word and \(n\)  is the length of \(\mathbf {S}\) . These vectors \(\mathbf {x}_i\)  are passed to a bi-directional LSTM [1]} (Bi-LSTM) to obtain the hidden representation \(\mathbf {h}_i^E\) . We set the hidden dimension of the forward and backward LSTM of the Bi-LSTM to be \(d_h/2\)  to obtain \(\mathbf {h}_i^E \in \mathbb {R}^{d_h}\) , where \(d_h\)  is the hidden dimension of the sequence generator LSTM of the decoder described below.
